Investigate a Database Project¶

TMDB movies Database¶

The following code uses Pandas to import the TMDB movie database from a csv file provided by Kaggle. https://www.kaggle.com/tmdb/tmdb-movie-metadata.

The first five rows are shown to give an idea of what the data looks like.

# import necessary libarires
import numpy as np
import pandas as pd

# import data from csv file
df = pd.read_csv(r"C:\\Users\\rstau\\OneDrive\\Documents\\data_analysis\\tmdb-movies.csv")

Data Wrangling¶

The data required some cleaning before evaluating. the following actions are need for the data. when neccesary I have listed when I used the help of others code in order to produce the correct results and those references are listed.

remove duplicate values (reference sheet 1) stackoverflow.com remove duplicate values)
remove movies that have 0 revenue or 0 budget (reference sheet 2) stackoverflow.com remove 0 values)
drop columns that are not relevant for our analysis (reference sheet 3) hackersandslackers.com removing columns from dataframe)
assigned data types to certain varibles

#remove duplicate ID numbers
df = df.drop_duplicates(subset='id', keep="last")

#remove the entire row of values that have 0 for revenue or budget
df = df[(df != 0).all(1)]


#drop the columns that are not relevant for our analysis
df = df.drop(['imdb_id','homepage','tagline', 'overview', 'keywords'], axis = 1)

#assign neccessary data types to the following varibles
df = df.astype({'budget': int, 'revenue': int, 'budget_adj': int, 'revenue_adj': int})
df['release_date'] = pd.to_datetime(df['release_date'])
#style the dataframe to use currency when neccesary
df.style.format({'revenue': '${0:,.0f}', 'budget': '${0:,.0f}', 'budget_adj': '${0:,.0f}', 'revenue_adj': '${0:,.0f}'})

Questions that will be addressed:

Which movies have the longest and shortest run times?
Which directors earned the most money in their careers?
What is the average revenue, the max revenue and the name of the titles produced by those directors?
Which movies generated the most revenue all-time after adjusting for inflation?
Is the budget of a movie correlated to the revenue it generates?
Is the budget of a movie correlated to the popularity score it receives?
Has the amount of revenue increased over the years as technologies in the industry have been evolving? how does the analysis compare when we account for inflation?

Question #1¶

Which movies have the longest and shortest run times?

#finds the location of the movie with the longest runtime
i = df['runtime'].argmax()
#uses the location find to give details on whole movie
df.iloc[i]

id                                                                  43434
popularity                                                       0.534192
budget                                                           18000000
revenue                                                            871279
original_title                                                     Carlos
cast                    Edgar RamÃ­rez|Alexander Scheer|Fadi Abi Samra...
director                                                  Olivier Assayas
runtime                                                               338
genres                                       Crime|Drama|Thriller|History
production_companies    Egoli Tossell Film AG|Canal+|Arte France|Films...
release_date                                          2010-05-19 00:00:00
vote_count                                                             35
vote_average                                                          6.2
release_year                                                         2010
budget_adj                                                       18000000
revenue_adj                                                        871279
Name: 2107, dtype: object

#finds the location of the movie with the longest runtime
j = df['runtime'].argmin()
#uses the location find to give details on whole movie
df.iloc[j]

id                                                                  24914
popularity                                                       0.208637
budget                                                                 10
revenue                                                                 5
original_title                                                Kid's Story
cast                    Clayton Watson|Keanu Reeves|Carrie-Anne Moss|K...
director                                              Shinichiro Watanabe
runtime                                                                15
genres                                          Science Fiction|Animation
production_companies                                          Studio 4Â°C
release_date                                          2003-06-02 00:00:00
vote_count                                                             16
vote_average                                                          6.8
release_year                                                         2003
budget_adj                                                             11
revenue_adj                                                             5
Name: 5162, dtype: object

The longest run time was 338 minutes for a movie titled "Carlos" from 2010

The shortest run time was 15 minutes for a movie title "Kid's Story" from 2003

Question #2 and #3:¶

Which directors earned the most money in their careers?

what is the average revenue, the max revenue and the name of the titles produced by those directors?

two seperate data frames were created using the groupby function and then merged together. data frame df1 was created to find the top 10 revenue earning directors and the statistics associated with that.

The second data frame, df2, was used to bring the title of the movies for the directors in the list, making it helpful to make connections to the films that earned the revenue. The code .apply(lambda x: ','.join(x)) was referenced from examples on geeksforgeeks.org as a way to join text. (reference sheet: 4) geeksforgeeks.org joining text)

In this analysis I focused on the overal total revenue earned, but other variables such as average or max could also be work analyzing.

# top 10 directors sorted by total revenue earned
df1 = (df.groupby('director')['revenue'].agg(['sum','mean','max','count']).sort_values(ascending=False, by='sum'))
# connecting all the titles that are associated with each director
df2 = df.groupby('director')['original_title'].apply(lambda x: ', '.join(x))

# merging the two databases into one table
directors_top_10 = pd.merge(df1,df2, how='inner', left_index=True, right_index=True).head(10)
# formatting variables that are currencies. (reference sheet: 5) stackoverflow.org. currency formatting in python)
directors_top_10.style.format({'sum': '${0:,.0f}', 'mean': '${0:,.0f}', 'max': '${0:,.0f}'})

#plot bar plot of merged dataframes
directorsplot = directors_top_10.plot.bar()
directorsplot.set_title("Top Revenue Producing Directors of All-time")
directorsplot.set_ylabel('Revenue in the millions of US dollars (witout inlation)')

Text(0, 0.5, 'Revenue in the millions of US dollars (witout inlation)')

The table and graph from the code show the top 10 directors. Steven Spilberg is by far the biggest for overall sum of revenue, which could be influenced by the amount of movies he has produced. also worthing noting in the results is the numbers for J.J Abrams. His max for a movie produced was significantly highier. when we look at the movies of J.J abrams we see that Star wars was the one that earned alot.

Having the movies of the director included in the table helps make sense of why the data looks the way it does.

Question #4:¶

Which movies generated the most revenue all-time after adjusting for inflation?

Similar groupby function was used in this question, however in this analysis I used the adjusted for inflation revenue numbers in order to make a list of the 10 most successful movies produced.

# grouping by original titles, sorting by the highiest revenue earning
df_top_10_movies = df.groupby(['original_title','director','release_year'])['revenue_adj'].agg(['max']).sort_values(ascending=False, by='max').head(10)
 
# formatting currency values    
df_top_10_movies.style.format('${0:,.2f}')

The interesting results of this table is that Steven Spielberg appears 3 times as the top earning movies. That would explain our previous findings of steven spilberg being the top earning director of all time.

Question #5:¶

Is the budget of a movie correlated to the revenue it generates?

the fuction below was created to calculate the correlation coefficient for two variables. The code for the function follow the example of some code shared on stackoverflow.com. (references 6: 4 stackoverflow.com calculate the correlation) https://stackoverflow.com/questions/48587192/i-have-written-a-code-to-calculate-the-correlation-between-two-pandas-series-ca

the correlation coefficient measures the linear relationship between two data sets. a value of +1 or -1 imply an exact linear relationship, while 0 implys no correlation.

Also include as part of this function is creating a scatterplot of the values.

# function created to calculate correlation coefficient and create a scatter plot
def correlation (x, y):
    std_x = (x - x.mean()) / x.std(ddof=0)
    std_y = (y - y.mean()) / y.std(ddof=0)
    
    %pylab inline
    import matplotlib.pyplot as plt

    plt.scatter(x, y,s=5)
           
    return (std_x * std_y).mean()

# running correlation function
correlation(df['budget_adj'], df['revenue_adj'])
plt.title('corrleation between budget size and revenue')
plt.ylabel('revenue adjust for inflation (millions of US dollars)')
plt.xlabel('budget adjust for inflation (millions of US dollars)')

Populating the interactive namespace from numpy and matplotlib

Text(0.5, 0, 'budget adjust for inflation (millions of US dollars)')

The resulting value for correlation coefficient of 0.64.

Values of 0.7 or highier are usually an indication of a strong correlation, while .3 through .7 are considered moderate positive linear relationship. So .64 is almost a strong correlation, but not quite. Our conclusion from this analysis is that highier budgets, while sometimes may produce a movie will earn a larger revenue, it is not always the case.

Question #6:¶

Is the budget of a movie correlated to the popularity score it receives?

Using the same function that we created earlier called correlation, we will analysis the correlation between budget and popularity.

# running correlation function
correlation(df['budget_adj'], df['popularity'])
plt.title('corrleation between budget size and popularity score')
plt.ylabel('popularity score')
plt.xlabel('budget adjust for inflation (millions of US dollars)')

Populating the interactive namespace from numpy and matplotlib

Text(0.5, 0, 'budget adjust for inflation (millions of US dollars)')

The results of the analysis are even weaker than the budget/revenue analysis in question #4. The value of .51 indicates on a moderate linear relationship. important to note is that correlation does not imply causation. the value of .51 indicates to me that there are other variables that are impacting the popularity score, possibly more so than budget.

Question #7:¶

Has the amount of revenue increased over the years as technologies in the industry have been evolving? how does the analysis compare when we account for inflation?

This analysis will require the following table and 3 graphs in order to get the besult understanding of the data.

first, the table shows the mean value for revenue generated for movies released in each given year. It's important for these values to be accounting for inflation. also worth noting is the count of movies released each year has significantly increased.

# calculates average revenue (adjust for inflation) for each year, and the count of movies produced. sorted 
# from latest to earliest.
df_release_year = df.groupby(["release_year"])['revenue_adj'].agg(['mean', 'count']).sort_values(ascending=False, by='release_year')

#formats revenue variable to currency
df_release_year.style.format({'mean': '${0:,.2f}'})

I'll discuss this table at the end. Secondly, the scatterplot below shows the yearly mean values that were calculated in the the table above.

The code used to create the scatterplot was create based from a learning example found on a webpage on bsdmag.org. (reference #7: bsdmag.org creating a historgram)

#sets up configuration for a scatter plot
fig = plt.figure()
ax = fig.add_subplot(1,1,1)

#prints scatter plot of each years average movies revenue (adjusted for inflation)
ax.scatter(df_release_year.index,df_release_year['mean'])
plt.title('Average Movies Revenue per year')
plt.ylabel('Revenue in the millions of US dollars')
plt.xlabel('Years')

Text(0.5, 0, 'Years')

Important to note here is that in the early part of the movie industry, the mean values varied widely year to year. this is due to the smaller year samples of movies.

A big take away from this scatter plot is that after adjusting for inflation, the revenue for the average movie has actually decreased over the years. Possibly explanations could be that movies produced in the later part of the lifetime of the movie industry had more forms of entertainment to compete with for revenue and were less of a wide spread phenomenom.

Lastly, I wanted to see what the effects of adjusting for inflation were. I produced 2 box and whisker plots that can be used to compare un-adjusted revenue values vs adjusted for inflation revenue values.

for the sake of readability, the data was grouped by decades, using the for loop code below. Also, Outliers were removed from the data using showfliers=False.

chart 1 is un-adjusted. chart 2 is adjusted.

*note: code used for producing the decade value was created with help found on stackoverflow.com (reference 8: stackoverflow.com seperating list of years into decades)

** note: in a example from stackoverflow.com a user suggested using the seaborn function to create these desired box plots. (reference 9: stackoverflow.com seaborn library for boxplots)

#creates a new variable in the df dataframe called decade
for year in df:
    df['decade'] = numpy.floor(df['release_year'] / 10) * 10
    df['decade'] = df['decade'].astype(int)

#creates a boxplot grouped by decades, excluding outliers which shows revenue statistics when not accounting for inflation    
import seaborn as sns      
boxplot1 = sns.boxplot(y='revenue', x='decade', data=df, showfliers=False)
#format plot
boxplot1.set_title('Revenue Without Adjusting for Inflation')
boxplot1.set_ylabel('Revenue in the millions of US dollars')

Text(0, 0.5, 'Revenue in the millions of US dollars')

#creates a boxplot grouped by decades, excluding outliers which shows revenue statistics when accounting for inflation
boxplot2 = sns.boxplot(y='revenue_adj', x='decade', data=df, showfliers=False)
#format plot
boxplot2.set_title('Revenue Adjusting for Inflation')
boxplot2.set_ylabel('Revenue in the millions of US dollars')

Text(0, 0.5, 'Revenue in the millions of US dollars')

conclusion¶

The trends are what I was expecting, however still interesting. Without adjusting for revenue one would think that later produced movies are the most successful. That is not the case with the adjusted values which results look similar to the scatter plot shown above, indicating a downward trend.

limitations¶

revenue shouldn't be the all end factor to determine popularity of a movie, especially when comparing across this large of a timeframe. the movie industry has changed alot over these decades and may not have the same audiences and demographics. for example, a popular movie in the early decades were more family friendly and could appeal to all demographics while a movie later could still be popular, but not as sutible for children, so appeals to a smaller demographic.

References:¶

1) stackoverflow.com remove duplicate values: https://stackoverflow.com/questions/12497402/python-pandas-remove-duplicates-by-columns-a-keeping-the-row-with-the-highest

2) stackoverflow.com remove 0 values: https://stackoverflow.com/questions/27020312/drop-row-in-pandas-dataframe-if-any-value-in-the-row-equals-zero/27020741

3) hackersandslackers.com removing columns from dataframe: https://hackersandslackers.com/pandas-dataframe-drop/

4) geeksforgeeks.org joining text: https://www.geeksforgeeks.org/join-two-text-columns-into-a-single-column-in-pandas/

5) stackoverflow.com currency formatting in python: https://stackoverflow.com/Questions/320929/currency-formatting-in-python

6) stackoverflow.com calculate the correlation: https://stackoverflow.com/questions/48587192/i-have-written-a-code-to-calculate-the-correlation-between-two-pandas-series-ca

7) bsdmag.org creating a historgram: https://bsdmag.org/wp-content/uploads/2016/06/infographics-final.jpg

8) stackoverflow.com seperating list of years into decades) https://stackoverflow.com/questions/31481589/separating-list-of-years-into-decades

9) stackoverflow.com seaborn library for boxplots: https://stackoverflow.com/questions/25284859/grouping-boxplots-in-seaborn-when-input-is-a-dataframe

	sum	mean	max	count	original_title
director
Steven Spielberg	$9,018,563,772	$334,020,880	$920,100,000	27	Bridge of Spies, Close Encounters of the Third Kind, A.I. Artificial Intelligence, Indiana Jones and the Kingdom of the Crystal Skull, The Adventures of Tintin, War Horse, Catch Me If You Can, Minority Report, Lincoln, Amistad, The Lost World: Jurassic Park, The Color Purple, War of the Worlds, Munich, The Terminal, 1941, Indiana Jones and the Temple of Doom, Raiders of the Lost Ark, E.T. the Extra-Terrestrial, Saving Private Ryan, Indiana Jones and the Last Crusade, Always, Hook, The Sugarland Express, Jaws, Schindler's List, Jurassic Park
Peter Jackson	$6,523,244,659	$593,022,242	$1,118,888,979	11	The Hobbit: The Battle of the Five Armies, The Lovely Bones, The Lord of the Rings: The Fellowship of the Ring, The Lord of the Rings: The Two Towers, Heavenly Creatures, The Hobbit: An Unexpected Journey, The Lord of the Rings: The Return of the King, The Hobbit: The Desolation of Smaug, King Kong, Braindead, The Frighteners
Michael Bay	$4,917,208,171	$447,018,925	$1,123,746,996	11	Transformers: Age of Extinction, Transformers: Revenge of the Fallen, Pearl Harbor, Transformers: Dark of the Moon, Bad Boys II, Pain & Gain, The Island, Transformers, Bad Boys, The Rock, Armageddon
Christopher Nolan	$4,167,548,502	$520,943,563	$1,081,041,287	8	Interstellar, Inception, The Dark Knight, Insomnia, The Dark Knight Rises, Batman Begins, The Prestige, Memento
David Yates	$4,154,295,625	$1,038,573,906	$1,327,817,822	4	Harry Potter and the Half-Blood Prince, Harry Potter and the Deathly Hallows: Part 1, Harry Potter and the Deathly Hallows: Part 2, Harry Potter and the Order of the Phoenix
Robert Zemeckis	$3,869,690,869	$257,979,391	$677,945,399	15	The Walk, A Christmas Carol, Forrest Gump, Flight, Contact, Back to the Future, The Polar Express, Used Cars, Beowulf, Romancing the Stone, Cast Away, What Lies Beneath, Back to the Future Part II, Who Framed Roger Rabbit, Back to the Future Part III
Chris Columbus	$3,725,631,503	$413,959,056	$976,475,550	9	Pixels, Percy Jackson & the Olympians: The Lightning Thief, Bicentennial Man, Harry Potter and the Philosopher's Stone, Harry Potter and the Chamber of Secrets, Rent, Home Alone 2: Lost In New York, Home Alone, Mrs. Doubtfire
Tim Burton	$3,665,414,624	$229,088,414	$1,025,467,110	16	Big Eyes, Alice in Wonderland, Sleepy Hollow, Planet of the Apes, Ed Wood, Dark Shadows, Frankenweenie, Big Fish, Pee-wee's Big Adventure, Charlie and the Chocolate Factory, Sweeney Todd: The Demon Barber of Fleet Street, Batman Returns, Mars Attacks!, Batman, Beetlejuice, Edward Scissorhands
Ridley Scott	$3,584,430,808	$170,687,181	$595,380,321	21	The Martian, Exodus: Gods and Kings, Robin Hood, Black Hawk Down, Hannibal, Body of Lies, Prometheus, G.I. Jane, The Counselor, Legend, Kingdom of Heaven, A Good Year, American Gangster, Alien, 1492: Conquest of Paradise, White Squall, Gladiator, Blade Runner, Black Rain, Thelma & Louise, Someone to Watch Over Me
J.J. Abrams	$3,579,169,916	$715,833,983	$2,068,178,225	5	Star Wars: The Force Awakens, Star Trek, Super 8, Star Trek Into Darkness, Mission: Impossible III

			max
original_title	director	release_year
Jaws	Steven Spielberg	1975	$1,907,005,841.00
Star Wars: The Force Awakens	J.J. Abrams	2015	$1,902,723,129.00
E.T. the Extra-Terrestrial	Steven Spielberg	1982	$1,791,694,309.00
The Net	Irwin Winkler	1995	$1,583,049,536.00
One Hundred and One Dalmatians	Clyde Geronimi\|Hamilton Luske\|Wolfgang Reitherman	1961	$1,574,814,739.00
The Avengers	Joss Whedon	2012	$1,443,191,435.00
The Empire Strikes Back	Irvin Kershner	1980	$1,424,626,187.00
Jurassic World	Colin Trevorrow	2015	$1,392,445,892.00
Jurassic Park	Steven Spielberg	1993	$1,388,863,311.00
Furious 7	James Wan	2015	$1,385,748,801.00

	mean	count
release_year
2015	$150,666,739.32	160
2014	$134,367,474.47	165
2013	$126,627,975.24	180
2012	$145,373,726.14	158
2011	$114,029,683.53	199
2010	$122,496,407.17	178
2009	$97,625,815.31	174
2008	$114,990,277.53	167
2007	$118,810,960.97	165
2006	$100,105,715.14	169
2005	$110,220,544.11	163
2004	$129,468,349.19	147
2003	$144,573,764.49	121
2002	$137,552,813.96	127
2001	$134,835,978.13	121
2000	$130,810,063.30	106
1999	$128,095,155.72	116
1998	$134,149,850.25	92
1997	$105,396,475.06	90
1996	$124,785,187.62	86
1995	$145,367,112.98	81
1994	$154,779,714.18	62
1993	$129,489,752.36	72
1992	$149,138,581.57	53
1991	$139,715,251.04	50
1990	$146,470,596.55	53
1989	$157,685,420.73	51
1988	$101,151,225.53	57
1987	$115,039,557.28	46
1986	$95,095,904.73	48
1985	$111,898,313.85	41
1984	$120,269,365.64	42
1983	$140,560,951.45	31
1982	$187,346,634.88	26
1981	$128,862,901.97	30
1980	$152,102,896.78	23
1979	$221,779,303.62	16
1978	$230,306,198.12	17
1977	$96,547,273.47	19
1976	$191,566,134.87	15
1975	$380,559,321.00	9
1974	$244,983,310.23	13
1973	$96,766,558.92	12
1972	$313,460,405.00	8
1971	$155,875,174.77	13
1970	$280,804,104.64	11
1969	$341,359,886.75	4
1968	$154,788,903.89	9
1967	$367,986,931.62	13
1966	$113,852,463.80	5
1965	$634,036,929.00	5
1964	$330,034,444.71	7
1963	$209,483,843.17	6
1962	$212,474,020.57	7
1961	$246,362,189.40	10
1960	$190,229,932.80	5

	id	popularity	budget	revenue	original_title	cast	director	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
0	135397	32.985763	$150,000,000	$1,513,528,810	Jurassic World	Chris Pratt\|Bryce Dallas Howard\|Irrfan Khan\|Vincent D'Onofrio\|Nick Robinson	Colin Trevorrow	124	Action\|Adventure\|Science Fiction\|Thriller	Universal Studios\|Amblin Entertainment\|Legendary Pictures\|Fuji Television Network\|Dentsu	2015-06-09 00:00:00	5562	6.500000	2015	$137,999,939	$1,392,445,892
1	76341	28.419936	$150,000,000	$378,436,354	Mad Max: Fury Road	Tom Hardy\|Charlize Theron\|Hugh Keays-Byrne\|Nicholas Hoult\|Josh Helman	George Miller	120	Action\|Adventure\|Science Fiction\|Thriller	Village Roadshow Pictures\|Kennedy Miller Productions	2015-05-13 00:00:00	6185	7.100000	2015	$137,999,939	$348,161,292
2	262500	13.112507	$110,000,000	$295,238,201	Insurgent	Shailene Woodley\|Theo James\|Kate Winslet\|Ansel Elgort\|Miles Teller	Robert Schwentke	119	Adventure\|Science Fiction\|Thriller	Summit Entertainment\|Mandeville Films\|Red Wagon Entertainment\|NeoReel	2015-03-18 00:00:00	2480	6.300000	2015	$101,199,955	$271,619,025
3	140607	11.173104	$200,000,000	$2,068,178,225	Star Wars: The Force Awakens	Harrison Ford\|Mark Hamill\|Carrie Fisher\|Adam Driver\|Daisy Ridley	J.J. Abrams	136	Action\|Adventure\|Science Fiction\|Fantasy	Lucasfilm\|Truenorth Productions\|Bad Robot	2015-12-15 00:00:00	5292	7.500000	2015	$183,999,919	$1,902,723,129
4	168259	9.335014	$190,000,000	$1,506,249,360	Furious 7	Vin Diesel\|Paul Walker\|Jason Statham\|Michelle Rodriguez\|Dwayne Johnson	James Wan	137	Action\|Crime\|Thriller	Universal Pictures\|Original Film\|Media Rights Capital\|Dentsu\|One Race Films	2015-04-01 00:00:00	2947	7.300000	2015	$174,799,923	$1,385,748,801
5	281957	9.110700	$135,000,000	$532,950,503	The Revenant	Leonardo DiCaprio\|Tom Hardy\|Will Poulter\|Domhnall Gleeson\|Paul Anderson	Alejandro GonzÃ¡lez IÃ±Ã¡rritu	156	Western\|Drama\|Adventure\|Thriller	Regency Enterprises\|Appian Way\|CatchPlay\|Anonymous Content\|New Regency Pictures	2015-12-25 00:00:00	3929	7.200000	2015	$124,199,945	$490,314,247
6	87101	8.654359	$155,000,000	$440,603,537	Terminator Genisys	Arnold Schwarzenegger\|Jason Clarke\|Emilia Clarke\|Jai Courtney\|J.K. Simmons	Alan Taylor	125	Science Fiction\|Action\|Thriller\|Adventure	Paramount Pictures\|Skydance Productions	2015-06-23 00:00:00	2598	5.800000	2015	$142,599,937	$405,355,075
7	286217	7.667400	$108,000,000	$595,380,321	The Martian	Matt Damon\|Jessica Chastain\|Kristen Wiig\|Jeff Daniels\|Michael PeÃ±a	Ridley Scott	141	Drama\|Adventure\|Science Fiction	Twentieth Century Fox Film Corporation\|Scott Free Productions\|Mid Atlantic Films\|International Traders\|TSG Entertainment	2015-09-30 00:00:00	4572	7.600000	2015	$99,359,956	$547,749,654
8	211672	7.404165	$74,000,000	$1,156,730,962	Minions	Sandra Bullock\|Jon Hamm\|Michael Keaton\|Allison Janney\|Steve Coogan	Kyle Balda\|Pierre Coffin	91	Family\|Animation\|Adventure\|Comedy	Universal Pictures\|Illumination Entertainment	2015-06-17 00:00:00	2893	6.500000	2015	$68,079,970	$1,064,192,016
9	150540	6.326804	$175,000,000	$853,708,609	Inside Out	Amy Poehler\|Phyllis Smith\|Richard Kind\|Bill Hader\|Lewis Black	Pete Docter	94	Comedy\|Animation\|Family	Walt Disney Pictures\|Pixar Animation Studios\|Walt Disney Studios Motion Pictures	2015-06-09 00:00:00	3935	8.000000	2015	$160,999,929	$785,411,574
10	206647	6.200282	$245,000,000	$880,674,609	Spectre	Daniel Craig\|Christoph Waltz\|LÃ©a Seydoux\|Ralph Fiennes\|Monica Bellucci	Sam Mendes	148	Action\|Adventure\|Crime	Columbia Pictures\|Danjaq\|B24	2015-10-26 00:00:00	3254	6.200000	2015	$225,399,900	$810,220,283
11	76757	6.189369	$176,000,003	$183,987,723	Jupiter Ascending	Mila Kunis\|Channing Tatum\|Sean Bean\|Eddie Redmayne\|Douglas Booth	Lana Wachowski\|Lilly Wachowski	124	Science Fiction\|Fantasy\|Action\|Adventure	Village Roadshow Pictures\|Dune Entertainment\|Anarchos Productions\|Warner Bros.	2015-02-04 00:00:00	1937	5.200000	2015	$161,919,931	$169,268,630
12	264660	6.118847	$15,000,000	$36,869,414	Ex Machina	Domhnall Gleeson\|Alicia Vikander\|Oscar Isaac\|Sonoya Mizuno\|Corey Johnson	Alex Garland	108	Drama\|Science Fiction	DNA Films\|Universal Pictures International (UPI)\|Film4	2015-01-21 00:00:00	2854	7.600000	2015	$13,799,993	$33,919,845
13	257344	5.984995	$88,000,000	$243,637,091	Pixels	Adam Sandler\|Michelle Monaghan\|Peter Dinklage\|Josh Gad\|Kevin James	Chris Columbus	105	Action\|Comedy\|Science Fiction	Columbia Pictures\|Happy Madison Productions	2015-07-16 00:00:00	1575	5.800000	2015	$80,959,964	$224,146,025
14	99861	5.944927	$280,000,000	$1,405,035,767	Avengers: Age of Ultron	Robert Downey Jr.\|Chris Hemsworth\|Mark Ruffalo\|Chris Evans\|Scarlett Johansson	Joss Whedon	141	Action\|Adventure\|Science Fiction	Marvel Studios\|Prime Focus\|Revolution Sun Studios	2015-04-22 00:00:00	4304	7.400000	2015	$257,599,886	$1,292,632,336
15	273248	5.898400	$44,000,000	$155,760,117	The Hateful Eight	Samuel L. Jackson\|Kurt Russell\|Jennifer Jason Leigh\|Walton Goggins\|DemiÃ¡n Bichir	Quentin Tarantino	167	Crime\|Drama\|Mystery\|Western	Double Feature Films\|The Weinstein Company\|FilmColony	2015-12-25 00:00:00	2389	7.400000	2015	$40,479,982	$143,299,244
16	260346	5.749758	$48,000,000	$325,771,424	Taken 3	Liam Neeson\|Forest Whitaker\|Maggie Grace\|Famke Janssen\|Dougray Scott	Olivier Megaton	109	Crime\|Action\|Thriller	Twentieth Century Fox Film Corporation\|M6 Films\|Canal+\|EuropaCorp\|CinÃ©+	2015-01-01 00:00:00	1578	6.100000	2015	$44,159,980	$299,709,578
17	102899	5.573184	$130,000,000	$518,602,163	Ant-Man	Paul Rudd\|Michael Douglas\|Evangeline Lilly\|Corey Stoll\|Bobby Cannavale	Peyton Reed	115	Science Fiction\|Action\|Adventure	Marvel Studios	2015-07-14 00:00:00	3779	7.000000	2015	$119,599,947	$477,113,780
18	150689	5.556818	$95,000,000	$542,351,353	Cinderella	Lily James\|Cate Blanchett\|Richard Madden\|Helena Bonham Carter\|Holliday Grainger	Kenneth Branagh	112	Romance\|Fantasy\|Family\|Drama	Walt Disney Pictures\|Genre Films\|Beagle Pug Films\|Allison Shearmur Productions	2015-03-12 00:00:00	1495	6.800000	2015	$87,399,961	$498,963,025
19	131634	5.476958	$160,000,000	$650,523,427	The Hunger Games: Mockingjay - Part 2	Jennifer Lawrence\|Josh Hutcherson\|Liam Hemsworth\|Woody Harrelson\|Elizabeth Banks	Francis Lawrence	136	War\|Adventure\|Science Fiction	Studio Babelsberg\|StudioCanal\|Lionsgate\|Walt Disney Studios Motion Pictures\|Color Force	2015-11-18 00:00:00	2380	6.500000	2015	$147,199,935	$598,481,289
20	158852	5.462138	$190,000,000	$209,035,668	Tomorrowland	Britt Robertson\|George Clooney\|Raffey Cassidy\|Thomas Robinson\|Hugh Laurie	Brad Bird	130	Action\|Family\|Science Fiction\|Adventure\|Mystery	Walt Disney Pictures\|Babieka\|A113	2015-05-19 00:00:00	1899	6.200000	2015	$174,799,923	$192,312,729
21	307081	5.337064	$30,000,000	$91,709,827	Southpaw	Jake Gyllenhaal\|Rachel McAdams\|Forest Whitaker\|Oona Laurence\|50 Cent	Antoine Fuqua	123	Action\|Drama	Escape Artists\|Riche-Ludwig Productions	2015-06-15 00:00:00	1386	7.300000	2015	$27,599,987	$84,373,003
22	254128	4.907832	$110,000,000	$470,490,832	San Andreas	Dwayne Johnson\|Alexandra Daddario\|Carla Gugino\|Ioan Gruffudd\|Archie Panjabi	Brad Peyton	114	Action\|Drama\|Thriller	New Line Cinema\|Village Roadshow Pictures\|Warner Bros.\|Flynn Picture Company	2015-05-27 00:00:00	2060	6.100000	2015	$101,199,955	$432,851,374
23	216015	4.710402	$40,000,000	$569,651,467	Fifty Shades of Grey	Dakota Johnson\|Jamie Dornan\|Jennifer Ehle\|Eloise Mumford\|Victor Rasuk	Sam Taylor-Johnson	125	Drama\|Romance	Focus Features\|Trigger Street Productions\|Michael De Luca Productions	2015-02-11 00:00:00	1865	5.300000	2015	$36,799,983	$524,079,119
24	318846	4.648046	$28,000,000	$133,346,506	The Big Short	Christian Bale\|Steve Carell\|Ryan Gosling\|Brad Pitt\|Melissa Leo	Adam McKay	130	Comedy\|Drama	Paramount Pictures\|Plan B Entertainment\|Regency Enterprises	2015-12-11 00:00:00	1545	7.300000	2015	$25,759,988	$122,678,731
25	177677	4.566713	$150,000,000	$682,330,139	Mission: Impossible - Rogue Nation	Tom Cruise\|Jeremy Renner\|Simon Pegg\|Rebecca Ferguson\|Ving Rhames	Christopher McQuarrie	131	Action	Paramount Pictures\|Skydance Productions\|China Movie Channel\|Bad Robot\|TC Productions	2015-07-23 00:00:00	2349	7.100000	2015	$137,999,939	$627,743,451
26	214756	4.564549	$68,000,000	$215,863,606	Ted 2	Mark Wahlberg\|Seth MacFarlane\|Amanda Seyfried\|Jessica Barth\|Giovanni Ribisi	Seth MacFarlane	115	Comedy	Universal Pictures\|Media Rights Capital\|Fuzzy Door Productions	2015-06-25 00:00:00	1666	6.300000	2015	$62,559,972	$198,594,430
27	207703	4.503789	$81,000,000	$403,802,136	Kingsman: The Secret Service	Taron Egerton\|Colin Firth\|Samuel L. Jackson\|Michael Caine\|Mark Strong	Matthew Vaughn	130	Crime\|Comedy\|Action\|Adventure	Twentieth Century Fox Film Corporation\|Marv Films\|TSG Entertainment\|Cloudy Productions	2015-01-24 00:00:00	3833	7.600000	2015	$74,519,967	$371,497,801
28	314365	4.062293	$20,000,000	$88,346,473	Spotlight	Mark Ruffalo\|Michael Keaton\|Rachel McAdams\|Liev Schreiber\|John Slattery	Tom McCarthy	128	Drama\|Thriller\|History	Participant Media\|Open Road Films\|Anonymous Content\|Rocklin / Faust\|Entertainment One Features	2015-11-06 00:00:00	1559	7.800000	2015	$18,399,991	$81,278,719
29	294254	3.968891	$61,000,000	$311,256,926	Maze Runner: The Scorch Trials	Dylan O'Brien\|Kaya Scodelario\|Thomas Brodie-Sangster\|Giancarlo Esposito\|Aidan Gillen	Wes Ball	132	Action\|Science Fiction\|Thriller	Gotham Group\|Temple Hill Entertainment\|TSG Entertainment	2015-09-09 00:00:00	1849	6.400000	2015	$56,119,975	$286,356,245
31	198184	3.899557	$49,000,000	$102,069,268	Chappie	Sharlto Copley\|Dev Patel\|Ninja\|Yolandi Visser\|Jose Pablo Cantillo	Neill Blomkamp	120	Crime\|Action\|Science Fiction	Columbia Pictures\|Media Rights Capital\|Sony Pictures Entertainment (SPE)\|Alpha Core\|Genre Films	2015-03-04 00:00:00	1990	6.600000	2015	$45,079,980	$93,903,685
32	254470	3.877764	$29,000,000	$287,506,194	Pitch Perfect 2	Anna Kendrick\|Rebel Wilson\|Hailee Steinfeld\|Brittany Snow\|Skylar Astin	Elizabeth Banks	115	Comedy\|Music	Universal Pictures\|Gold Circle Films\|Brownstone Productions	2015-05-07 00:00:00	1264	6.800000	2015	$26,679,988	$264,505,582
33	296098	3.648210	$40,000,000	$162,610,473	Bridge of Spies	Tom Hanks\|Mark Rylance\|Amy Ryan\|Alan Alda\|Sebastian Koch	Steven Spielberg	141	Thriller\|Drama	DreamWorks SKG\|Amblin Entertainment\|Studio Babelsberg\|Fox 2000 Pictures\|Marc Platt Productions	2015-10-15 00:00:00	1638	7.100000	2015	$36,799,983	$149,601,569
34	257445	3.644541	$58,000,000	$150,170,815	Goosebumps	Jack Black\|Dylan Minnette\|Odeya Rush\|Amy Ryan\|Jillian Bell	Rob Letterman	103	Adventure\|Horror\|Comedy	Columbia Pictures\|Original Film\|Scholastic Entertainment	2015-08-05 00:00:00	600	6.200000	2015	$53,359,976	$138,157,089
35	264644	3.557846	$6,000,000	$35,401,758	Room	Brie Larson\|Jacob Tremblay\|Joan Allen\|Sean Bridgers\|William H. Macy	Lenny Abrahamson	117	Drama\|Thriller	Element Pictures\|No Trace Camping\|A24\|Duperele Films	2015-10-16 00:00:00	1520	8.000000	2015	$5,519,997	$32,569,603
37	105864	3.339135	$175,000,000	$331,926,147	The Good Dinosaur	Raymond Ochoa\|Jack Bright\|Jeffrey Wright\|Frances McDormand\|Maleah Nipay-Padilla	Peter Sohn	93	Adventure\|Animation\|Comedy\|Family\|Fantasy	Walt Disney Pictures\|Pixar Animation Studios\|Buena Vista	2015-11-14 00:00:00	1087	6.600000	2015	$160,999,929	$305,371,920
38	241554	3.237370	$50,000,000	$71,561,644	Run All Night	Liam Neeson\|Ed Harris\|Joel Kinnaman\|Boyd Holbrook\|Bruce McGill	Jaume Collet-Serra	114	Action\|Crime\|Drama\|Mystery\|Thriller	Vertigo Entertainment\|Energy Entertainment\|RatPac-Dune Entertainment	2015-03-11 00:00:00	814	6.300000	2015	$45,999,979	$65,836,683
39	167073	3.227329	$11,000,000	$62,076,141	Brooklyn	Saoirse Ronan\|Domhnall Gleeson\|Emory Cohen\|Emily Bett Rickards\|Eileen O'Higgins	John Crowley	111	Drama\|Romance	Wildgaze Films\|Irish Film Board\|Item 7\|Parallel Film Productions	2015-11-04 00:00:00	754	7.300000	2015	$10,119,995	$57,110,024
40	277216	3.202719	$28,000,000	$201,634,991	Straight Outta Compton	O'Shea Jackson Jr.\|Corey Hawkins\|Jason Mitchell\|Neil Brown Jr.\|Aldis Hodge	F. Gary Gray	147	Drama\|Music	New Line Cinema\|Universal Pictures\|Legendary Pictures\|Cube Vision\|Broken Chair Flickz	2015-08-13 00:00:00	918	7.700000	2015	$25,759,988	$185,504,110
41	274854	3.080505	$90,000,000	$140,396,650	The Last Witch Hunter	Vin Diesel\|Rose Leslie\|Michael Caine\|Elijah Wood\|Ã“lafur Darri Ã“lafsson	Breck Eisner	106	Fantasy\|Action\|Adventure	Summit Entertainment\|Atmosphere Entertainment MM\|One Race Films\|Goldmann Pictures\|NeoReel	2015-10-21 00:00:00	879	5.600000	2015	$82,799,963	$129,164,861